Microblog semantic context retrieval system based on linked open data and graph-based theory

Kalloubi, Fahd; Nfaoui, El Habib; Beqqali, Omar El

doi:10.1016/j.eswa.2016.01.020

Cited by 21 publications

(13 citation statements)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, microblogging retrieval systems suffer from the problems of data sparseness and the semantic gap. To overcome those limitations and to contextualize the semantic meaning of microblog content, recent studies ( [62], [71], and [117]) focus on exploiting the use of social semantics and user-generated content by identifying entities in them. These entities are used as keywords to indicate the topics of the messages, description of real-life events, as well as to reveal behavioral patterns and building interest profiles, thus enabling the interrelation of semantically related terms and the social proximity or similarity between profiles and interests.…”

Section: Discussionmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

View full text Add to dashboard Cite

The discovery of influential entities in all kinds of networks (e.g. social, digital, or computer) has always been an important field of study. In recent years, Online Social Networks (OSNs) have been established as a basic means of communication and often influencers and opinion makers promote politics, events, brands or products through viral content. In this work, we present a systematic review across i) online social influence metrics, properties, and applications and ii) the role of semantic in modeling OSNs information. We found that both areas can jointly provide useful insights towards the qualitative assessment of viral user-generated content, as well as for modeling the dynamic properties of influential content and its flow dynamics.

show abstract

Section: Discussionmentioning

confidence: 99%

Modeling Influence with Semantics in Social Networks

2020

View full text Add to dashboard Cite

show abstract

“…Consequently, the application systems that use these relationships of microblog have been developed. These applications include microblog semantic context retrieval systems [11], top-k followee recommendation over microblog systems [5], expert finding systems using follower relation, user-list relation [39], and hot event detection systems monitoring the cost-effective nodes in microblog platforms [7]. All application systems do not come down to mine and analyse the persona features from the micro posts.…”

Section: Hot Persona Mining (Hpm) Algorithmmentioning

confidence: 99%

“…Two recent aspect studies based on link structure graphs [19] and microblog semantic context [11] are used to recommend followees, followers, interest friends, influence persons, and others.…”

Section: Introductionmentioning

confidence: 99%

Mining Hot-Personae Approach Based on Local Social Microblog Graph

Du¹,

Peng

Su³

et al. 2019

ITC

View full text Add to dashboard Cite

With the increasing popularity of online social media platforms, netizens always chat with their friends and share information, such as what they like in their daily lives, on these platforms. Netizens publish tons of information on social platforms every day. These platforms converge many people and information. The processes by which the publishers find the sharers who are interested in their publications and the sharers find some interesting things and information in what the publishers published have resulted in the challenge of retrieving information from social network fields. To address these issues, we propose a novel algorithm, named Hot Persona Mining, to analyze the users' focus personae from microblog posts in the online social networks. During mining, we first utilize local-based graph clustering to establish the nearest neighbor nodes of target users. Then, we mine users' focused personae entities from their neighbors' published microblog posts in different periods. Then, we construct the users' active score vector and their interest matrix to mine the hot personae in every local social graph. The experimental results show that our algorithm effectively mines current focus of the target user, and exhibits good performance as shown by its precision, recall and F-measures.

show abstract

“…A semantic based information retrieval system based on graph theory was proposed by Kalloubi et al in [5]. This technique operates based on graph-of-concepts, rather than the conventional bag-of-concepts.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Context Based Information Retrieval Using Semantic and Features on Streaming Content

Karthikeyan¹

2016

IJRET

View full text Add to dashboard Cite

Information retrieval has become one of the major necessities in the current information age. Appropriate information retrieval is still complex due to data heterogeneity and unconventional modes of data dissemination. This paper presents a context based information retrieval architecture to perform real time retrieval of data. This model operates on the semantic similarity of the data, rather than the content similarity. Hence this technique exhibits better and efficient retrieval levels providing high reliability. The retrieved documents are pre-processed and feature vectors are created from the tokens. Polarity matching is used to filter semantically correlated results and magnitude based result ranking is performed to perform further elimination of inappropriate results. Experiments conducted using the proposed model exhibits very high true retrieval rates, along with high precision and recall levels exhibiting the competence of the proposed work.

show abstract

Microblog semantic context retrieval system based on linked open data and graph-based theory

Cited by 21 publications

References 7 publications

Modeling Influence with Semantics in Social Networks

Modeling Influence with Semantics in Social Networks

Mining Hot-Personae Approach Based on Local Social Microblog Graph

Real-Time Context Based Information Retrieval Using Semantic and Features on Streaming Content

Contact Info

Product

Resources

About